Interaction Techniques for Providing Sensitive Location Data of Interpersonal Violence with User-Defined Privacy Preservation

要旨

Violence is a significant public health issue. Interventions to reduce violence rely on data about where incidents occur. Cities have historically used incomplete law enforcement crime data, but many are shifting toward data collected from hospital patients via the Cardiff Model to form a more complete understanding of violence. Still, location data is wrought with issues related to completeness, quality, and privacy. For example, if a patient feels that sharing a detailed location may present them with additional risks, such as undesired police involvement or retaliatory violence, they may be unwilling or unable to share. Consequently, survivors of violence who are the most vulnerable may remain the most at risk. We have designed a user interface and mapping algorithm to confront these challenges and conducted an experiment with emergency department patients. The results indicate a significant improvement in location data obtained using the interface compared to the existing screening interview.

受賞
Honorable Mention
著者
Alex Godwin
American University, Washington, District of Columbia, United States
Jasmine C. Foriest
Georgia Institute of Technology, Atlanta, Georgia, United States
Mia Bottcher
Emory University, Atlanta, Georgia, United States
Gretchen Baas
George Mason University, Fairfax, Virginia, United States
Michael Tsai
Duke University, Durham, North Carolina, United States
Daniel T. Wu
Emory University School of Medicine, Atlanta, Georgia, United States
DOI

10.1145/3706598.3714136

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714136

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Risk and Privacy

G302
6 件の発表
2025-04-29 20:10:00
2025-04-29 21:40:00
日本語まとめ
読み込み中…